DynamoDB development services for applications where single-digit latency at millions of requests per second isn't optional. Access patterns modelled before the first table is created. Partition keys that distribute evenly — not keys that create hot partitions and throttle at 30% of provisioned capacity. We build on DynamoDB with the data modelling discipline that turns a serverless database into a predictable one.
Why Entalogics for DynamoDB
The DynamoDB tables we audit always have the same problems — a partition key chosen by field name instead of access pattern, five GSIs where two would suffice, scan operations on hot paths because nobody modelled the query, and on-demand pricing burning money that provisioned mode would save. DynamoDB is simple to start. It's not simple to get right.
DynamoDB is the opposite of relational — you design for your queries, not your entities. We document every access pattern before creating a table. Every key, every GSI, every sort key justified by a real query your application makes.
A bad partition key creates hot partitions that throttle at 30% of table capacity. We model for high cardinality and even distribution. Composite keys, write sharding, and hierarchical keys where single attributes don't distribute evenly enough.
On-demand for unpredictable traffic. Provisioned with reserved capacity for stable baselines — 3–4x cheaper per request. We measure your traffic pattern and choose the mode that matches. GSIs sized with sparse projections, not ALL.
Single-table design co-locates related data for fewer round trips. But it also increases complexity, makes onboarding harder, and can bloat GSIs. We use single-table for genuinely related entities and separate tables where independent scaling matters more.
When DynamoDB, when not
DynamoDB gives you predictable millisecond latency at any scale with zero operational overhead. It also punishes bad data models harder than any relational database. We'll tell you on the first call if DynamoDB fits.
PICK DYNAMODB WHEN
CONSIDER ALTERNATIVES WHEN
WE SAY NO WHEN
What we build on DynamoDB
The shapes of DynamoDB development we deliver most. Each modelled for access patterns from day one.
Lambda + DynamoDB for event-driven APIs. Single-table design co-locating related entities. Sub-5ms reads on every hot path.
Millions of events per second with composite partition keys distributing writes evenly. TTL for automatic expiration. DynamoDB Streams for real-time processing.
Tenant-isolated data with tenant ID as the partition key prefix. Per-tenant query scoping without cross-partition reads. GSIs for admin-level views.
DynamoDB as a managed session store or cache layer. TTL for auto-expiry. DAX for microsecond read caching on hot items.
Existing tables with throttling or runaway costs. Partition key analysis, GSI consolidation, capacity mode right-sizing, and reserved capacity where stable.
SQL data remodelled for DynamoDB access patterns. Entities denormalised. Relationships handled via composite keys and GSIs. Migration with performance validation.
The playbook
DynamoDB patterns from real production tables — not workshop exercises.
P01
Every query listed. Every key justified. Every GSI mapped to a real access pattern. No table created on assumption.
P02
Related entities share a partition key. Sort key distinguishes type and enables range queries. One Query returns everything your screen needs.
P03
GSIs project only the attributes the query needs. Items without the GSI attributes don't appear in the index. Less storage, less write amplification, lower cost.
P04
Write to one table, stream changes to Lambda, write denormalised views to another table or GSI. Every hot-path query reads from a pre-computed structure.
P05
Enabled on every production table. Most-accessed partition keys visible in real time. Hot partitions caught before they throttle.
P06
Provisioned mode with reserved capacity for predictable baselines. On-demand for spiky traffic. Mixed across tables based on actual patterns. 3–4x savings over pure on-demand.
Signature case
A B2C e-commerce platform on DynamoDB — scan operations on the product listing page, five GSIs with ALL projections, on-demand pricing at $4,800/mo, and throttling on the order table during flash sales. Remodelled to single-table design with sparse GSIs, switched hot tables to provisioned with reserved capacity, eliminated all scan operations in 7 weeks. Monthly cost dropped to $1,900. Zero throttling during the next flash sale.
Before
Scan on product listing · 5 GSIs with ALL projection · on-demand $4,800/mo · throttling during sales
After
Query-only, partition-aligned · 2 sparse GSIs · provisioned + reserved $1,900/mo · zero throttling
Engagement shape
A typical DynamoDB engagement. We model and deploy table by table — the current data stays live while we work.
Two senior DynamoDB engineers. Access pattern analysis, partition key audit, GSI review, capacity mode assessment. A ranked, dollarized RFC.
Access patterns modelled, keys designed, GSIs defined, first table deployed with SDK integration. Real throughput measured.
Each table modelled, migrated, validated. Streams wired. Capacity mode optimised. Your application keeps running.
Contributor Insights enabled. Cost dashboard live. Runbook handed to your team — or we stay on retainer.
Stack
Our default DynamoDB development stack — picked for production.
Engagement
No hourly retainer that bills for "thinking time." Pick a lane that matches your stage; everything is fixed-quote or transparently rated.
A defined scope, a fixed price, a senior-only team. From data modelling to production in 6–10 weeks.
$15k–$30k
FIXED SCOPE
Embedded engineers in your team. Senior data architects specialising in DynamoDB modelling. Pause, resize, end with 30 days' notice.
$5k / eng / mo
PER ENGINEER
A long-term partner for serverless data — cost optimisation, single-table architecture, migration strategy, hiring help.
custom
PROCUREMENT-FRIENDLY
Founder-direct
Thirty minutes with the founder. We'll bring a senior DynamoDB engineer, the relevant playbook, and a candid read on whether DynamoDB is the right database — or whether PostgreSQL, Aurora, or Cosmos DB fits your workload better.
